State of AI 2026 report: Key data, findings, and insights

One of the clearest indicators of a field’s maturity is its size and growth.

According to ABI Research, the global artificial intelligence (AI) software market is projected to reach $174 billion in 2025 and grow to $467 billion by 2030, reflecting a compound annual growth rate of about 22%.

ABI Research also notes that the generative AI (GenAI) software market is expanding even faster, with a 29% CAGR, rising from $63.7 billion in 2025 to $220 billion by 2030.

The faster growth of GenAI means its share within the overall AI software market will increase from 37% in 2025 to 47% by 2030.

From a regional perspective, North America dominates the global AI software market in 2025, holding a 54% share. The Asia-Pacific region, driven mainly by China, follows with 33%.

By 2030, we anticipate that this balance will shift. As China deepens its engagement in the AI race with the United States, the Asia-Pacific region’s share is projected to rise to 47%, while North America’s share may fall to 33%.

When it comes to GenAI, China is on track to nearly match North America by 2030, with forecasts of $70.4 billion and $72.6 billion, respectively.

One reason for that is China’s significantly higher growth rate of 45.1%, one of the strongest in the world. North America’s rate, at 17%, is the lowest due to its larger starting base.

Europe is the only region expected to grow even faster, with a 45.5% CAGR.

While the North American market is likely to double in value over the next five years, the Chinese market could expand 5.5 times, and the European market may grow sixfold.

In this report, spending means all major investment areas: hardware, software, services, and embedded AI in both consumer and enterprise products. The aim is to show how money is being allocated across the entire AI ecosystem.

Gartner estimates that total worldwide AI spending will reach nearly $1.5 trillion in 2025, grow to over $2 trillion in 2026, and rise to $3.3 trillion by 2029, with a compound annual growth rate of about 22%.

Vention’s research shows that while AI is perceived as a way of reducing costs, companies are ready to invest up to 30% more into specialized, high-impact AI use cases. This is also supported by global research.

Spending on hardware and infrastructure is projected to exceed software and services, accounting for around 59% of total expenditures during this period.

All categories are showing steady year-over-year growth in absolute terms, although some are experiencing a decline in relative share.

For example, AI services spending fell from 26% in 2024 to 19% in 2025 and is expected to drop to 16% in 2026. Other areas, including AI application software and AI infrastructure software, are gaining ground, increasing from 8% and 6% in 2024 to 13% and 11% in 2026.

According to Gartner, these two segments are expected to drive the next wave of growth.

AI investments in 2025 reached $225.8 billion, surpassing previous records of $114.9 billion in 2021 and $114.4 billion in 2024.

What’s more, AI companies made up about 48% of total equity funding in 2025, even though they represent only 23% of total deals. In other words, one in five venture deals and one in two invested dollars went to AI.

In the United States, AI’s share of funding and deals is even greater. According to SVB, AI companies accounted for 58% of all capital invested and 36% of total deals in 2025. In 2024, these shares were around 30%, and in 2022, they were close to 20%.

AI investments have been growing rapidly over the past few years, capturing a larger share of total funding while the average deal size continues to rise.

Quarterly data confirms this trend. Although the total investment volume rose sharply over the last three quarters, the number of deals remained steady, averaging 1,200 to 1,500 per quarter.

North America continues to dominate AI venture investments. In 2025, the region captured 87% of all capital raised.

Europe ranks second with 8%, which is twice the share of Asia, accounting for 4%. Although Asia’s AI software market is quickly approaching North America’s in size, investment levels in Asian AI companies remain far lower.

The average deal size in H1 2025 is more than twice as high as in 2021 and 2024.

This is driven by major funding rounds from companies such as:

• OpenAI: $40 billion in March
• Anthropic: $1 billion in January and $3.5 B in March (with an additional $13 billion in September not yet included in calculations)
• Anduril: $2.5 billion in February and $2.5 B in June
• Anysphere: $2.3 billion in November
• Infinite Reality: $3 billion in January
• Safe Superintelligence: $2 billion in April
• Reflection.Ai: $2 billion in October
• Scale AI: $14.8 billion in June
• xAI: $5 billion in June

The median deal size in H1 2025 hasn’t yet reached the 2021 record, but has increased by nearly 30% compared with 2024.

AI companies continue to attract more capital than those outside the field. According to SVB and PitchBook, funding rounds raised by AI companies are, on average, 17% to 115% larger than those of non-AI companies, depending on the stage of investment. The difference is most visible at the Seed and Series D stages.

While a non-AI Series D company typically raises about $100 million, an AI company at the same stage raises approximately $150 million.

Big Tech companies continue to dominate AI investments. According to Crunchbase, during the first two quarters of 2025, Amazon, Meta, Nvidia, Google, and Microsoft invested more than $90 billion in AI startups. Together, they accounted for more than 40% of the total global AI investment volume.

• Of the $90 billion, about $40 billion came from OpenAI’s funding round in March. The round included Microsoft among 16 co-investors, with SoftBank as the lead investor.
• Meta resumed active AI investing after a four-year pause (2020–2023), allocating $11 billion across three deals in 2024 and more than $14 billion through one major deal with Scale AI in 2025.
• Amazon scaled back its AI investments after two years of heavy activity. Following over $10 billion in spending in 2023 and 2024, the company committed less than $200 million in 2025.
• Nvidia is emerging as the new leader in AI investment. Having spent $20.6 billion in 2024, they continue gaining momentum with $27.7 billion of investments in 2025, outpacing most peers in strategic AI funding.

For major tech players, investing heavily in AI startups is a strategic move to secure influence over the future AI stack, not just a plain interest.

Frontier AI development demands enormous compute power and infrastructure. Cloud providers such as Microsoft, Amazon, and Google back startups to integrate them into their ecosystems through cloud credits, exclusive contracts, and revenue-sharing agreements. In effect, their investments often come back as long-term service commitments.

Big Tech companies can also afford high upfront risks and losses across several ventures. Their scale and regulatory reach help them shape markets, partnerships, and distribution channels in ways that strengthen their position.

To sum it up, Big Tech’s AI funding serves two purposes: fueling external innovation and channeling future infrastructure, data, and software spending back into their core businesses.

With all these deals and spending, how widely has AI managed to spread its influence across the globe? To answer that, we look at the latest AI adoption statistics across regions, industries, and company sizes.

Over half of American adults (61%) have used AI in the past six months. Nearly one in five relies on it every day.

A KPMG study conducted in early 2025 shows similar trends across the globe. About 21% of the world’s population uses AI tools daily, and 66% use them at least every few months.

One of the most striking findings on AI adoption can be found in a joint study by OpenAI, Duke University, and Harvard University, which was published in September 2025.

As of July 2025, ChatGPT was used weekly by roughly 700 million people, representing roughly 10% of the global adult population.

By October 2025, during the OpenAI DevDay keynote, Sam Altman announced that the number of ChatGPT’s weekly active users had already reached 800 million. A year earlier, in October 2024, it was 200 million, and in November 2023, 100 million.

Within one year, the number of weekly active ChatGPT users has quadrupled, and over two years, it has increased eightfold.

OpenAI’s report also provides valuable insights into how people use AI tools:

• In everyday contexts, the top three uses are seeking practical guidance, finding information, and writing.
• In work settings, these three remain on top, followed closely by technical help (ranks fourth among overall usage patterns).

AI adoption across businesses continues to rise.

Our research revealed that 93% of companies are already using AI. 80% use it directly, and the other 13% benefit from AI through a vendor.

McKinsey’s findings showed that 88% of organizations already use AI in at least one of their business functions, with the share of those using GenAI growing to 79%.

It’s also worth noting that the share of companies using AI in multiple business functions is increasing even more rapidly.

More than half of all organizations now deploy AI in three or more business processes.

The most common areas include marketing and sales, product and service development, service operations, and software engineering.

AI adoption is expanding across nearly every industry. It now plays a key role in banking, insurance, manufacturing, retail, healthcare, life sciences, education, and even government services.

The AI technology with the highest adoption rate is generative AI, as it’s used by an average of 81.3% of organizations across these industries.

Quantum AI is the least utilized, with an average adoption rate of 28.4%.

While the adoption rate of quantum AI is still relatively low, the fact that over a quarter of respondents are already exploring ways to merge quantum computing and AI suggests growing demand for faster and more efficient infrastructure.

When looking at AI adoption by geography, developing economies now lead the way.

According to KPMG, the top ten countries by percentage of regular AI users are all emerging markets, including India, Nigeria, Egypt, China, Brazil, Mexico, Argentina, and Colombia.

While developed economies, particularly the United States, continue to drive AI innovation and production, everyday use is expanding faster in developing regions.

In other words, the tools built in advanced economies are increasingly being adopted, tested, and scaled in emerging ones, which shows a global balance between innovation and widespread practical application.

• In the UK, 46.9% of decision-makers see faster development speed as the key benefit.
• In DACH, 45.9% of respondents state that they mostly benefit from AI task automation.
• Among mature companies, task automation is also the prime motivator, with 40.5% of questioned companies stating it as the primary benefit.

The growing focus on GenAI shows how strongly companies now connect it with everyday performance. Leaders see AI as a way to help teams work faster, use information more effectively, and cut time spent on repetitive tasks, all of which drive productivity.

Only 19% of respondents said AI boosted their ROI by more than 5%, while 75% reported low-to-zero gains so far.

When it comes to costs, 60% saw either no change or an increase below 10%;  23% experienced a drop of up to 19% in operating costs.

But the future looks more optimistic.

Over the next three years, only 10% of executives expect no change in revenue from AI.

At the same time, 51% anticipate growth of more than 5%, showing that most companies view AI as a long-term investment rather than a quick boost.

The World Economic Forum’s Future of Jobs Report 2025 offers a look at how the job market will evolve through 2030.

Two of the three fastest-growing roles are linked to AI and data: big data specialists and AI or machine learning engineers.

The report also finds that AI and related information-processing technologies will have a net positive effect on global employment, creating more jobs than they displace.

The fastest-declining jobs are tied to manual or repetitive work that can be easily automated.

We expect roles such as data entry clerks, cashiers, and postal service clerks to shrink the most.

In contrast, AI-driven and tech-enabled jobs continue to expand, which highlights the growing importance of digital skills across industries.

While the overall impact of AI on the job market is considered net-positive, our own research shows that 76% of decision-makers believe they won’t have to hire as many software developers this year. Among the most cut roles, junior engineers and developers are prevalent, with 41% of respondents mentioning them.

AI and other emerging technologies are expected to strengthen the labor market overall, but workers in automation-prone roles will need to reskill to stay relevant. Many organizations already encourage AI-related training to prepare for this transition.

According to KPMG, 83% of professionals are interested in learning more about AI.

However, only 21% rate their AI knowledge as high, and just 39% have taken any AI-related courses.

In June 2025, Coursera reported that enrollments in GenAI courses grew 195% year over year, surpassing 8 million total learners.

On average, 700 courses on the platform attracted 12 new enrollments every minute.

While global demand for GenAI courses more than doubled, Latin America recorded the sharpest rise (a 425% surge in enrollments), followed by Africa (+134%) and North America (+135%).

Businesses and consumers alike have already made AI part of their everyday lives.

According to a KPMG study, 72% of respondents accept AI, 58% view it as trustworthy, and 46% say they trust AI systems on a deeper level.

Similar to adoption and education trends, acceptance levels are notably higher in emerging economies. At the same time, respondents expressed concerns about potential downsides of AI use.

The most common worries include cybersecurity threats, loss of human connection, misinformation, privacy risks, and job displacement.

Many also mentioned issues such as dependency, overreliance on automation, and the risk of bias or unfair treatment, which underlines the importance of building AI systems that are both transparent and responsible.

As the AI market grows, so does attention from malicious actors. AI is already used in sensitive areas like code generation, asset management, and autonomous systems. Tools such as Lovable and Firebase Studio simplify development by providing full-stack prototyping environments that allow users to launch products without writing code.

However, such rapid development and wide accessibility introduce various new risks. The most common ones are advanced prompt injections, poor model security, and untested integrations.

Research published by Apiiro on September 5, 2025, shows both benefits and challenges of using AI in software development.

While AI improves code quality overall, the likelihood of high-level issues, such as privilege mismanagement or architectural flaws, has significantly increased. Agentic browsers, such as OpenAI’s Atlas or Perplexity’s Comet, create additional entry points for targeted attacks.

Experts from Brave recently uncovered a flaw in Perplexity’s filters that allowed attackers to trick the AI into revealing sensitive information.

According to Gartner, in 2024 and early 2025, 32% of cybersecurity leaders reported at least one attack involving AI applications where prompts were manipulated for malicious purposes.

AI-driven security risks extend beyond code.

Approximately 62% of organizations have experienced deepfake attacks that involve social engineering or the manipulation of automated systems, such as biometric verification.

Such incidents show how AI-powered deception is quickly becoming a mainstream cybersecurity threat.

As AI adoption accelerates, the need for responsible practices grows at a similar pace. Responsible AI (RAI) focuses on building and using AI systems in lawful, ethical, and reliable ways.

Interest in RAI is growing worldwide, driven by stronger cooperation between regulators, increased academic research, and growing demand for transparency from users. In 2024, the number of scholarly papers on AI governance reached 1,278, up from 992 in 2023, which reflects how central the topic has become.

AI governance platforms now rank second among strategic technology priorities for 2025.

Organizations adopting them are expected to achieve 25% better regulatory compliance by 2028 and as much as a 30% increase in customer trust.

MarketsandMarkets projects that the AI governance market will grow from $890 million in 2024 to $5.8 billion by 2029, representing an annual growth rate of about 45%.

Companies are already investing in operationalizing RAI. McKinsey’s 2024 survey found that both large enterprises and smaller firms are dedicating a growing share of their budgets to responsible AI initiatives, treating them as long-term strategic priorities rather than compliance exercises.

The biggest challenge companies face is not the funding itself, but the shortage of AI-specific knowledge and training.

Resource constraints came in second, followed by regulatory uncertainty and technical limitations.

To sum it up, while organizations are eager to adopt RAI, success depends on equipping teams with the right expertise to apply these frameworks effectively.

With the rapid growth around AI, it’s natural to wonder whether we’re seeing another bubble form, similar to the dot-com boom of the 1990s.

If you compare Nasdaq trends from that period with today’s AI-driven market data, the similarities are easy to spot. Early excitement, heavy investment, and expectations running ahead of reality. That part of the cycle definitely looks familiar.

Launching an AI product on its own is no longer enough. What matters now is whether it solves real problems, fits into existing workflows, and holds up in everyday use.

And we at Vention see this clearly. Conversations have moved away from “Can we add AI?” to “How does AI change how we build, ship, and maintain software?” That’s where approaches like retrieval-augmented generation (RAG), AI embedded directly into the software delivery lifecycle (SDLC), and more fluid development styles, often referred to as vibe coding, start to matter.

These are not surface-level additions because they reshape how teams think, plan, and deliver. RAG helps AI work with trusted knowledge. AI in the SDLC changes how ideas move from requirements to release. Vibe coding reflects a broader shift toward faster iteration, clearer intent, and closer collaboration between people and tools.

Even if a market correction comes, companies with a clear direction and practical approach will continue to move forward. The dot-com bubble didn’t kill the internet. Instead, it cleared the noise and set the foundation for what followed.

What’s changing now is how AI is used. The focus is moving away from standalone models and demos toward systems built into everyday workflows.

At Vention, we’ve been working this way for years. We apply these approaches internally and with clients, treating AI as part of the delivery system rather than something layered on top. That early focus is now becoming the norm, as what once felt experimental turns into standard practice across the industry.

Teams that integrate AI thoughtfully into their workflows will move faster with fewer surprises, while short-term or careless implementations won’t last.

Change will continue, and no one can predict every shift. What matters is staying practical, learning quickly, and building systems that can adapt over time.

AI is not replacing strong engineering or good judgment. It’s amplifying them.